The present disclosure pertains generally to near-field microwave imaging.
In the art of microwave detection, near-field microwave imaging attempts to detect the profile of an object less than one wavelength to several wavelengths away from the antennas by measuring an electromagnetic scattered field. Typically, many antennas are placed near the object and antennas take turns transmitting a waveform that illuminates the object, while the other antennas serve as receivers. Alternatively, the detection can use a small number of movable antennas to observe the object in multiple locations. After detection, an algorithm is applied to process the collected data to form an image displaying the object's profile. Typical applications are buried-object detection, nondestructive surveys, and biomedical examinations.
There are two main approaches to active microwave imaging: microwave tomography and RADAR-based imaging. Microwave tomography involves reconstructing an image in terms of a quantitative description of any objects present such as a dielectric constant or conductivity, impedance, or local velocity. This approach usually is ill posed and is performed by iteratively comparing measurement data with numerical simulation data, which can be a slow and time consuming process. In contrast to microwave tomography, RADAR-based imaging methods reconstruct an image in terms of a qualitative description of any objects present and instead aim to find the profile of an object. More specifically, the purpose of a RADAR-based method is to distinguish the object's size, shape, and location instead of showing a distribution of a physical parameter in the entire area.
FIG. 3A, FIG. 3B, and FIG. 6 can help describe how a conventional RADAR based method works, in which the time arrival information and amplitude of scattered signals are utilized to identify the presence and location of a significant scatter. The conventional algorithm involves calculating the flight time of the signal travelling in space or a medium then back-propagating the signal to the transmitters' position with corresponding time-delay compensation. In this approach, a time domain UWB (ultra-wide band) signal is often used to illuminate an object and the scattered signal is measured from numerous places (in this case from three different places). The Euclidean distance from each receiver to the source through a focal point (a position in the area under exploration) is estimated and each signal is compensated for its time delay or shift. A flight time from positions in the interested area to antennas (or probes in simulation) is individually calculated. In locations with no objects present, the shifted signals are less correlated and will result in less amplitude when they are summed, as shown in FIG. 3A. In the object's location, the shifted signals are highly correlated in time and result in greater amplitude when summed, as shown in FIG. 3B. As a result, a qualitative image of the object under investigation is obtained. This kind of approach is expected to achieve a high-resolution imaging effect since it uses a UWB signal containing many frequency components.
A discussion of time domain confocal imaging algorithms is disclosed in “A Confocal Microwave Imaging Algorithm for Breast Cancer Detection” by Xu Li et al. in IEEE Microwave and Wireless Components Letters, Vol. 11, No. 3, March 2001 and “Enhancing Breast Tumor Detection with Near-Field Imaging” by Elise C. Fear et al. in IEEE Microwave Magazine, March 2002. The entire contents of these publications are incorporated herein by reference for such techniques as well as systems, methods and other techniques related to microwave imaging.